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glpn-nyu-finetuned-diode-221122-014502

Depth Estimation Transformers PyTorch glpn vision generated_from_trainer License: apache-2.0
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glpn-nyu-finetuned-diode-221122-014502

This model is a fine-tuned version of vinvino02/glpn-nyu on the diode-subset dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3476
  • Mae: 0.2763
  • Rmse: 0.4088
  • Abs Rel: 0.3308
  • Log Mae: 0.1161
  • Log Rmse: 0.1700
  • Delta1: 0.5682
  • Delta2: 0.8301
  • Delta3: 0.9279

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 24
  • eval_batch_size: 48
  • seed: 2022
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Mae Rmse Abs Rel Log Mae Log Rmse Delta1 Delta2 Delta3
0.7598 1.0 72 0.5809 0.7606 0.9281 0.9834 0.2597 0.3064 0.1320 0.3250 0.6234
0.4481 2.0 144 0.4013 0.3507 0.4879 0.4181 0.1415 0.1950 0.4427 0.7602 0.9021
0.4066 3.0 216 0.3706 0.3081 0.4484 0.3675 0.1269 0.1823 0.5187 0.7977 0.9148
0.3965 4.0 288 0.3641 0.2987 0.4336 0.3607 0.1239 0.1787 0.5294 0.8072 0.9205
0.3942 5.0 360 0.3582 0.2903 0.4251 0.3490 0.1207 0.1753 0.5466 0.8165 0.9232
0.3575 6.0 432 0.3568 0.2898 0.4184 0.3569 0.1211 0.1753 0.5390 0.8171 0.9265
0.3418 7.0 504 0.3490 0.2771 0.4178 0.3248 0.1156 0.1707 0.5783 0.8312 0.9259
0.2916 8.0 576 0.3512 0.2819 0.4172 0.3373 0.1178 0.1725 0.5620 0.8253 0.9262
0.3055 9.0 648 0.3506 0.2808 0.4091 0.3422 0.1180 0.1718 0.5537 0.8248 0.9292
0.2932 10.0 720 0.3518 0.2809 0.4110 0.3441 0.1182 0.1724 0.5548 0.8239 0.9290
0.2518 11.0 792 0.3476 0.2756 0.4115 0.3265 0.1155 0.1700 0.5741 0.8326 0.9264
0.3177 12.0 864 0.3491 0.2784 0.4104 0.3333 0.1169 0.1706 0.5620 0.8290 0.9283
0.3038 13.0 936 0.3503 0.2795 0.4094 0.3410 0.1175 0.1717 0.5596 0.8275 0.9283
0.3299 14.0 1008 0.3460 0.2750 0.4098 0.3257 0.1154 0.1693 0.5721 0.8325 0.9283
0.3325 15.0 1080 0.3476 0.2763 0.4088 0.3308 0.1161 0.1700 0.5682 0.8301 0.9279

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.12.1+cu116
  • Tokenizers 0.13.2
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Evaluation results

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